---
title: Quickstart
---

## Overview

ParadeDB Analytics transforms Postgres into a fast analytical query engine over external object stores like Amazon S3 and table
formats like CSV, Parquet, Delta, or Iceberg. Queries are pushed down to [DuckDB](https://duckdb.org/), which
delivers excellent analytical performance.

## Getting Started

The following example queries an example dataset of 3 million NYC taxi trips from January 2024, hosted in a
public S3 bucket provided by ParadeDB.

```sql
CREATE FOREIGN DATA WRAPPER parquet_wrapper
HANDLER parquet_fdw_handler VALIDATOR parquet_fdw_validator;

-- Provide S3 credentials
CREATE SERVER parquet_server FOREIGN DATA WRAPPER parquet_wrapper;

-- Create foreign table with auto schema creation
CREATE FOREIGN TABLE trips ()
SERVER parquet_server
OPTIONS (files 's3://paradedb-benchmarks/yellow_tripdata_2024-01.parquet');

-- Success! Now you can query the remote Parquet file like a regular Postgres table
SELECT COUNT(*) FROM trips;
  count
---------
 2964624
(1 row)
```

That's it! To query your own data, please refer to the [data import](/analytics/import) documentation.

<Note>
  If no columns are provided to `CREATE FOREIGN TABLE`, the appropriate Postgres
  schema will automatically be created.
</Note>

## For Further Assistance

The `paradedb.help` function opens a GitHub Discussion that the ParadeDB team will respond to.

```sql
SELECT paradedb.help(
  subject => $$Something isn't working$$,
  body => $$Issue description$$
);
```
